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Duration 21 hours
Course Outline
Foundations of TinyML in Healthcare
- Key attributes of TinyML systems
- Specific constraints and requirements in healthcare contexts
- Introduction to wearable AI architectures
Biosignal Acquisition and Preprocessing
- Handling physiological sensor data
- Methods for noise reduction and signal filtering
- Extracting features from medical time-series data
Developing TinyML Models for Wearables
- Choosing appropriate algorithms for physiological data
- Training models within constrained computational environments
- Assessing model performance on health-specific datasets
Deploying Models on Wearable Devices
- Leveraging TensorFlow Lite Micro for on-device inference
- Integrating AI models into medical wearable hardware
- Conducting testing and validation on embedded systems
Power and Memory Optimization
- Strategies to minimize computational overhead
- Refining data flow and memory utilization
- Achieving a balance between accuracy and efficiency
Safety, Reliability, and Compliance
- Regulatory implications for AI-enabled wearables
- Safeguarding system robustness and clinical usability
- Implementing fail-safe mechanisms and error management
Case Studies and Healthcare Applications
- Wearable systems for cardiac monitoring
- Activity recognition in rehabilitation settings
- Continuous tracking of glucose levels and biometrics
Future Directions in Medical TinyML
- Approaches to multi-sensor data fusion
- Personalized health analytics
- Next-generation low-power AI chipsets
Summary and Next Steps
Requirements
- A foundational grasp of core machine learning principles
- Practical experience with embedded or biomedical devices
- Proficiency in Python or C-based development
Target Audience
- Medical and healthcare professionals
- Biomedical engineers
- AI developers
Testimonials (2)
skills and projects in claude, variety of tools for different purpose
Agnieszka Czypionka
Course - Generative AI in Healthcare: Transforming Medicine and Patient Care
Comprehensiveness about the backround; how to do a prompt.
